Supervised dimensionality reduction

Supervised dimensionality reduction is a commonly used data processing technique in fields such as computer vision, aiming to reduce data dimensions by retaining features related to label information. Its core objective is to enhance the model's generalization ability and computational efficiency while reducing the risk of overfitting. By optimizing feature selection and transformation, supervised dimensionality reduction can effectively improve the performance of classification and regression tasks, enhancing the interpretability and visualization of data. In practical applications, this technique is widely used in image recognition, object detection, and other scenarios, helping to boost the overall performance and robustness of algorithms.